---
title: "Ohio COVID-19"
author: "Ying-Ju Tessa Chen"
output: 
  flexdashboard::flex_dashboard:
    theme: journal
    orientation: columns
    social: ["facebook", "twitter", "linkedin"]
    source_code: embed
---




```{r setup, include=FALSE}
library(flexdashboard)  ## you need this package to create dashboard
```

Basic Information
=======================================================================
Column  {data-width=400}
---
  
### Introduction
In this dashboard, we summarize the information from the data provided by Ohio Department of Health. 

In this data set, there are 8 variables. 

- **County**: 88 counties
- **Sex**: Female, Male, Unknown
- **Age Range**: 0-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70-79, 80+, Unknown 
- **Onset Date**
- **Date of Death**
- **Case Count**
- **Death Count**
- **Hospitalized Count**

  
```{r}
# load necessary packages
library(data.table)
library(ggplot2)
library(ggmap)
library(plotly)
library(plyr)
library(chron)
library(Hmisc)
library(stringr)
library(sp)
```
  
```{r}
df <- fread("https://coronavirus.ohio.gov/static/COVIDSummaryData.csv")
colnames(df) <- c("County", "Sex", "Age_Range", "Onset_Date",         
                  "Date_Of_Death", "Case_Count",        
                  "Death_Count", "Hospitalized_Count")

# remove the last row that shows the total count and make sure the type of each variable is correct                 
df <- as.data.frame(df[1:(nrow(df)-1),])
df[,1:3] <- lapply(df[,1:3], factor)
df[,4:5] <- lapply(df[,4:5], function(x)  as.Date(x, "%m/%d/%Y"))
df[,6:8] <- lapply(df[,6:8], as.numeric)

R1 <- c("Williams", "Defiance", "Paulding", "Van Wert", 
        "Mercer", "Fulton", "Henry", "Putnam", "Allen",
        "Auglaize", "Lucas", "Wood", "Hancock", "Ottawa",
        "Sandusky", "Seneca", "Erie", "Huron")
R2 <- c("Lorain", "Cuyahoga", "Geauga", "Lake", "Ashtabula")
R3 <- c("Darke", "Preble", "Shelby", "Miami", "Montgomery", 
        "Champaign", "Clark", "Greene")
R4 <- c("Crawford", "Delaware", "Fairfield", "Fayette", 
        "Franklin", "Hardin", "Knox", "Licking", "Logan",
        "Madison", "Marion", "Morrow", "Pickaway", "Union", "Wyandot")
R5 <- c("Richland", "Ashland", "Medina", "Wayne", "Holmes",
        "Summit", "Stark", "Tuscarawas", "Trumbull", "Portage",
        "Mahoning", "Columbiana", "Carroll")
R6 <- c("Butler", "Hamilton", "Warren", "Clermont", 
        "Clinton", "Highland", "Brown", "Adams")
R7 <- c("Ross", "Pike", "Scioto", "Hocking", "Vinton",
        "Jackson", "Lawrence", "Athens", "Meigs", "Gallia")
R8 <- c("Coshocton", "Muskingum", "Perry", "Morgan",
        "Guernsey", "Noble", "Washington", "Harrison",
        "Belmont", "Monroe", "Jefferson")

Zone1 <- c(R1, R2, R5)
Zone2 <- c(R4, R7, R8)
Zone3 <- c(R3, R6)
df$Zones <- c(NA)
df$Zones <- ifelse(df$County%in%Zone1, 1, df$Zones)
df$Zones <- ifelse(df$County%in%Zone2, 2, df$Zones)
df$Zones <- ifelse(df$County%in%Zone3, 3, df$Zones)
Hospitalized_Zones <- table(df$Zones, df$Hospitalized_Count)
Hospitalized_Zones_Cases <- apply(Hospitalized_Zones, 1, function(x) sum(x*as.numeric(colnames(Hospitalized_Zones))))
Cases_Zones <- table(df$Zones, df$Case_Count)
Cases_Zones_all <- apply(Cases_Zones, 1, function(x) sum(x*as.numeric(colnames(Cases_Zones))))
Death_Zones <- table(df$Zones, df$Death_Count)
Death_Zones_Cases <- apply(Death_Zones, 1, function(x) sum(x*as.numeric(colnames(Death_Zones))))
```
\

**Hospitalized Cases in Healthcare Zones**

- **Zone 1 (Cleveland Area):** `r unname(Hospitalized_Zones_Cases[1])` Cases (`r round(Hospitalized_Zones_Cases[1]/Cases_Zones_all[1]*100, 2)`%)
- **Zone 2 (Columbus Area):** `r unname(Hospitalized_Zones_Cases[2])` Cases (`r round(Hospitalized_Zones_Cases[2]/Cases_Zones_all[2]*100, 2)`%)
- **Zone 3 (Cincinnati/Dayton):** `r unname(Hospitalized_Zones_Cases[3])` Cases (`r round(Hospitalized_Zones_Cases[3]/Cases_Zones_all[3]*100, 2)`%)

\

**Death Cases in Healthcare Zones**

- **Zone 1 (Cleveland Area):** `r unname(Death_Zones_Cases[1])` Cases (`r round(Death_Zones_Cases[1]/Cases_Zones_all[1]*100, 2)`%)
- **Zone 2 (Columbus Area):** `r unname(Death_Zones_Cases[2])` Cases (`r round(Death_Zones_Cases[2]/Cases_Zones_all[2]*100, 2)`%)
- **Zone 3 (Cincinnati/Dayton):** `r unname(Death_Zones_Cases[3])` Cases (`r round(Death_Zones_Cases[3]/Cases_Zones_all[3]*100, 2)`%)

\

**Source:** cleveland.com. 

\

**Note:** All data displayed are based on the date of illness onset or the earliest known date associated with the case. All results reported are subject to change depend on the information provided by Ohio Department of Health.


Column {data-width=600}
---

```{r}
all_dates <- names(table(df$Onset_Date))
latest_date <- sort(df$Onset_Date, decreasing = TRUE)[1]
```

### Summary Statistics
**Last Updated: `r date()` EST**

**The latest onset date is `r format(latest_date, "%B %d, %Y")`.**

- Total Number of **Confirmed Cases**: `r sum(df$Case_Count)`
- Total Number of **Hospitalizations**: `r sum(df$Hospitalized_Count)`
- Total Number of **Deaths**: `r sum(df$Death_Count)`
- **Death Rate in Ohio**: `r paste0(round(sum(df$Death_Count)/sum(df$Case_Count)*100, 2), "%")`



### Distribution of Confirmed Cases by Age

```{r}
AGE_summary <- table(df$Age_Range)
AGE_count <- as.vector(unname(AGE_summary))
AGE <- data.frame(age=AGE_count, percent=paste0(round(AGE_count/sum(AGE_count)*100, 2), "%"))
rownames(AGE) <- names(AGE_summary)
colnames(AGE) <- c("Count", "Percent")
DT::datatable(t(AGE), options = list(
 columnDefs = list(list(className = 'dt-center', targets = 0:nrow(AGE)))
))
```


### Distribution of Confirmed Cases by Sex

```{r}
Sex_summary <- table(df$Sex)
Sex_count <- as.vector(unname(Sex_summary))
SEX <- data.frame(sex=Sex_count, percent=paste0(round(Sex_count/sum(Sex_count)*100, 2), "%"))
rownames(SEX) <- names(Sex_summary)
colnames(SEX) <- c("Count", "Percent")
DT::datatable(t(SEX), options = list(
 columnDefs = list(list(className = 'dt-center', targets = 0:nrow(SEX)))
))
```

Daily Cases
=======================================================================

Column {.tabset data-width=500}
-----------------------------------------------------------------------

```{r}
date_sum <- table(df$Onset_Date, df$Case_Count)
daily_cases <- apply(date_sum, 1, function(x) sum(x*as.numeric(colnames(date_sum))))

monthly <- data.frame(dates=as.Date(all_dates, "%Y-%m-%d"), cases=daily_cases)
rownames(monthly) <- c()

# the following function is from "https://stackoverflow.com/questions/7919998/basic-calendar-display-in-r"

cal <- function(month, year){
      ld <- seq.dates( from=julian(month,1,year), length=2, by='months')[2]-1
      days <- seq.dates( from=julian(month,1,year), to=ld)
      tmp <- month.day.year(days)
      wd <- do.call(day.of.week, tmp)
      cs <- cumsum(wd == 0)
      if(cs[1] > 0) cs <- cs - 1
      nr <- max(cs) + 1
      par(oma=c(0.1,0.1,4.6,0.1))
      par(mfrow=c(nr,7))
      par(mar=c(0,0,0,0))
      for(i in seq_len(wd[1])){ 
        plot.new()
        #box()
      }
      day.name <- c('Sun','Mon','Tues','Wed','Thur','Fri','Sat')
      for(i in tmp$day){
        plot.new()
        box()
        text(0,1, i, adj=c(0,1))
        if(i < 8) mtext( day.name[wd[i]+1], line=0.5,
                         at=grconvertX(0.5,to='ndc'), outer=TRUE ) 
      }
      mtext(month.name[month], line=2.5, at=0.5, cex=1.75, outer=TRUE)
}

week_days <- function(x){
  days <- c(1:7)
  names(days) <- c("Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday")
  days_index <- which(names(days)==x)
  return(unname(days[days_index]))
}
  
```

```{r , message=FALSE, echo=FALSE, cache=TRUE, error=FALSE, results='asis'}

for (i in month(latest_date):2){
    df_m <- monthly[which(month(monthly$dates)==i),]
    first_day <- weekdays(as.Date(paste0("2020-", i, "-01"), "%Y-%m-%d"))
    C_matrix <- matrix(NA, ncol=3, nrow=monthDays(as.Date(paste0("2020-", i, "-01"))))
    total_days <- week_days(first_day):(week_days(first_day)+monthDays(as.Date(paste0("2020-", i, "-01")))-1)
    C_matrix[,1] <- ceiling(total_days/7)
    C_matrix[,2] <- total_days%%7
    C_matrix[,2] <- ifelse(C_matrix[,2]==0, 7, C_matrix[,2])
    for (j in 1:nrow(df_m)){
      C_matrix[mday(df_m$dates[j]),3] <- df_m$cases[j]
    }

    cat('### ', month.abb[i],' \n')
    cal(i, 2020)
    for (k in mday(df_m$dates)){
        par(mfg=C_matrix[k,1:2])
        text(.5, .5, as.character(C_matrix[k,3]), cex=2)
    }
    cat('\n \n')
}
```

Column {data-width=500}
-----------------------------------------------------------------------

### Distribution of Daily Cases

```{r}
D <- data.frame(Dates=names(daily_cases), cases=unname(daily_cases))

p_dates <- plot_ly(D, x=~Dates, y=~cases, type="bar", text=as.character(cumsum(daily_cases)), name="", 
hovertemplate = paste('%{x}', '
Daily Cases: %{y:s}
', 
                      'Total Cases: %{text:s}'))
                   
p_dates <- p_dates %>% layout(uniformtext=list(minsize=8,mode='hide')) %>% config(displayModeBar = F)

p_dates
```


Distribution by Counties
=======================================================================

```{r}
find_cases <- function(var1, var2){
  cases <- table(var1, var2)
  all_cases <- apply(cases, 1, function(x) sum(x*as.numeric(colnames(cases))))
}
county_cases_all <- find_cases(df$County, df$Case_Count)
county_hospitalized <- find_cases(df$County, df$Hospitalized_Count)
county_death <- find_cases(df$County, df$Death_Count)
df_ohio_cases <- data.frame(county=names(county_cases_all), confirmed_count=county_cases_all, hospitalized_count=county_hospitalized, death_count=county_death)                     
rownames(df_ohio_cases) <- c()
usa <- map_data("county")  # get basic map data for all USA counties 
oh <- subset(usa, region == "ohio")  # subset to counties in Ohio 
oh$county = str_to_title(oh$subregion)
my.df = merge(oh, df_ohio_cases, by = "county", all.x = TRUE, 
              sort = FALSE)
#my.df$count <- ifelse(is.na(my.df$count), 0, my.df$count)
my.df = my.df[order(my.df$order), ]
getLabelPoint <- # Returns a county-named list of label points
  function(county) {Polygon(county[c('long', 'lat')])@labpt}
centroids = by(oh, oh$county, getLabelPoint)     # Returns list
centroids2 <- do.call("rbind.data.frame", centroids)  # Convert to Data Frame
centroids2$county = rownames(centroids)
names(centroids2) <- c('clong', 'clat', "county")                 # Appropriate Header

centroids3 <- merge(centroids2, df_ohio_cases, by="county", all.x=TRUE, sort=FALSE)
centroids3$confirmed_count <- ifelse(is.na(centroids3$confirmed_count), 0, centroids3$confirmed_count)
centroids3$hospitalized_count <- ifelse(is.na(centroids3$hospitalized_count), 0, centroids3$hospitalized_count)
centroids3$death_count <- ifelse(is.na(centroids3$death_count), 0, centroids3$death_count)
#centroids3$label <- paste0(centroids3$county,": ", centroids3$count, " Cases")
g <- ggplot(centroids3, aes(x = clong, y = clat, group = 1, 
                              text = paste0(county,":", 
                                           "
", confirmed_count, " comfired cases",
                                           "
", hospitalized_count, " hospitalized cases",
                                           "
", death_count, " death cases"),
))
g <- g + geom_polygon(data=my.df, 
                       aes(x=long, y=lat, group=group, fill = confirmed_count),
                       color="black", size = 0.2) +
  geom_text(data = centroids3, aes(x = clong, y = clat, label = county), color = "black", size = 4)+
  scale_fill_continuous(name="Confirmed Cases", low = "lightblue", 
                        high = "darkblue",limits = c(0,max(my.df$confirmed_count)), na.value = "grey50") +
  
  labs(title="Confirmed / Hospitalized / Death Cases in Ohio") +  theme(legend.position = "none", axis.title.x=element_blank(), axis.text.x=element_blank(),
        axis.ticks.x=element_blank(), axis.title.y=element_blank(),
        axis.text.y =element_blank(), axis.ticks.y=element_blank())

ggplotly(g, tooltip = "text") %>% layout(autosize = F, width = 1200, height = 800) 

```

Distribution by Age
=======================================================================

Column {data-width=500}
---

### Distribution of Confirmed Cases by the Age Range

**We excluded `r length(which(df$Age_Range=="Unknown"))` people whose age is unknown.**

\

```{r}
# remove the cases for which the age range is "Unknown"
if (length(which(df$Age_Range=="Unknown"))==0){
  df1 <- df
}else{
  df1 <- df[-which(df$Age_Range=="Unknown"),]
}

df1$Age_Range <- factor(df1$Age_Range)
# find counts and relative counts (%) in each age range
Age_Dist <- table(df1$Age_Range, df1$Case_Count)
n <- sum(apply(Age_Dist, 1, function(x) sum(x*as.numeric(colnames(Age_Dist)))))
Age_Percent <- round(apply(Age_Dist, 1, function(x) sum(x*as.numeric(colnames(Age_Dist))))/n*100,2)

# form a data frame for the summary information of AGE
df_age <- data.frame(Age_Range=levels(df1$Age_Range), Percent_Cases=Age_Percent, text1=paste0(Age_Percent, "%"))

# obtatin the bar chart for the distribution of Ohio's confirmed cases by the Age Range 
p_age <- plot_ly(df_age, x=~Age_Range, y=~Percent_Cases, type="bar",
                 text = df_age$text1, textposition = 'outside')%>% config(displayModeBar = F)
p_age <- p_age %>% layout(xaxis=list(title="Age Range"), yaxis=list(title="Percent of Cases"))

p_age %>% layout(autosize = F, width = 650, height = 650)
```


Column {data-width=500}
---

### Distribution of Death Cases by Age Range

**We excluded `r length(which(df$Age_Range=="Unknown"))` people whose age is unknown.**

\

```{r}
# find death counts and relative counts (%) in each age range
Age_Dist_Death <- table(df1$Age_Range, df1$Death_Count)
n <- sum(apply(Age_Dist_Death, 1, function(x) sum(x*as.numeric(colnames(Age_Dist_Death)))))
Age_Percent_Death <- round(apply(Age_Dist_Death, 1, function(x) sum(x*as.numeric(colnames(Age_Dist_Death))))/n*100,2)

# form a data frame for the summary information of AGE
df_age_death <- data.frame(Age_Range=levels(df1$Age_Range), Percent_Cases=Age_Percent_Death, text1=paste0(Age_Percent_Death, "%"))

# obtatin the bar chart for the distribution of Ohio's confirmed cases by the Age Range 
p_age_death <- plot_ly(df_age_death, x=~Age_Range, y=~Percent_Cases, type="bar",
                 text = df_age_death$text1, textposition = 'outside')%>% config(displayModeBar = F)
p_age_death <- p_age_death %>% layout(xaxis=list(title="Age Range"), yaxis=list(title="Percent of Death Cases"))

p_age_death %>% layout(autosize = F, width = 650, height = 650)
```